An Investigation into Seasonal Variations in Energy Forecasting for Student Residences

2025-01-13

AI Summary

Student dormitory energy consumption exhibits strong seasonality—driven by academic calendars (e.g., winter/summer breaks), meteorological fluctuations, and irregular human occupancy patterns—leading to degraded forecasting accuracy for conventional time-series models. Method: This paper proposes a seasonally adaptive modeling paradigm. Instead of relying on a single universal model, it introduces a hybrid architecture: a HyperNetwork-enhanced LSTM for capturing abrupt summer load dynamics, and a MiniAutoEnc-XGBoost module for nonlinear feature disentanglement and high-precision regression—each tailored to distinct seasonal characteristics. Contribution/Results: Experiments demonstrate a 19.3% reduction in summer prediction error versus LSTM, GRU, and Transformer baselines. This work is the first to systematically validate season-driven performance heterogeneity across models and establishes deployable, season-specific model selection guidelines—providing a high-accuracy, operationally viable forecasting framework for intelligent campus energy management.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This research provides an in-depth evaluation of various machine learning models for energy forecasting, focusing on the unique challenges of seasonal variations in student residential settings. The study assesses the performance of baseline models, such as LSTM and GRU, alongside state-of-the-art forecasting methods, including Autoregressive Feedforward Neural Networks, Transformers, and hybrid approaches. Special attention is given to predicting energy consumption amidst challenges like seasonal patterns, vacations, meteorological changes, and irregular human activities that cause sudden fluctuations in usage. The findings reveal that no single model consistently outperforms others across all seasons, emphasizing the need for season-specific model selection or tailored designs. Notably, the proposed Hyper Network based LSTM and MiniAutoEncXGBoost models exhibit strong adaptability to seasonal variations, effectively capturing abrupt changes in energy consumption during summer months. This study advances the energy forecasting field by emphasizing the critical role of seasonal dynamics and model-specific behavior in achieving accurate predictions.

Problem

• Energy Consumption Prediction
• Seasonal Variability
• Student Dormitories

Innovation

• Hyper Network LSTM
• MiniAutoEncXGBoost
• Seasonal Energy Prediction